The identification and matching of various patterns can be difficult and time intensive. For example, in the field of fingerprinting, the accuracy of the identification procedure relies on algorithms that perform direct feature comparisons. Once an algorithm has selected the “best candidates” then individual inspectors do a personal verification analysis before the fingerprint can be considered identified. In short, a direct comparison algorithm picks out “best candidates” and then the final selection is made through personal verification. Such algorithms are sensitive to position and variability in resolution between field data and file data. The time and data-processing infrastructure required for such identification is extensive as the operation is quite cumbersome. Also, the identification of patterns in contexts other than fingerprinting can be expensive and time consuming as well.